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Self-paced course

Paper Breakdowns

The “What the Paper Actually Says” series — close readings of the systems and ML papers that matter, focused on the mechanism, the numbers, and when not to use the idea.

20 lessons~4h totalFree
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Curriculum

  1. Cassandra: What the Paper Actually Says21 min read
  2. EAGLE: Speculative Decoding with Feature-Level Prediction — What the Paper Actually Says12 min read
  3. The Llama 3 Herd of Models: What the Paper Actually Says11 min read
  4. LLM.int8(): What the 8-bit Matrix Multiplication Paper Actually Says10 min read
  5. Mooncake: What the KV-Cache-Centric Disaggregated Serving Paper Actually Says14 min read
  6. Pregel: What the Large-Scale Graph Processing Paper Actually Says14 min read
  7. Switch Transformers: What the Sparse MoE Scaling Paper Actually Says13 min read
  8. Titans: What the Test-Time Memorization Paper Actually Says9 min read
  9. T5: What the Text-to-Text Paper Actually Says13 min read
  10. SGLang and RadixAttention: What the Paper Actually Says11 min read
  11. SARATHI: What the Chunked-Prefill Paper Actually Says11 min read
  12. Mixture of Depths: What the Paper Actually Says14 min read
  13. MapReduce: What the Google Paper Actually Says12 min read
  14. Mamba: What the Selective State Space Paper Actually Says11 min read
  15. Kafka: What the Original Paper Actually Says16 min read
  16. H2O: Heavy-Hitter Oracle for KV Cache Eviction — What the Paper Actually Says14 min read
  17. DeepSeek-V3: What the Frontier-on-a-Budget Paper Actually Says13 min read
  18. Dapper: What Google's Distributed Tracing Paper Actually Says13 min read
  19. Toolformer: What the Paper Actually Says11 min read
  20. MegaScale: What ByteDance's 12,288-GPU Training Paper Actually Says14 min read